Iterations for active sampling in inelastic neutron scattering

Finally, "Experimental_SPINW_wDMI.gif" represents the iterations during the fitting of the experimental spin wave data.

Methods

We combine Linear Spin Wave Theory (LSWT), Active Learning Sampling, and Adaptive Noise Reduction to form the Kalman Filter enhanced Adversarial Bayesian Optimization Algorithm (KFABO). This algorithm integrates two coupled Bayesian Optimization (BO) algorithms with a Kalman filter. The first BO algorithm, termed fBO, employs trust region Bayesian optimization (Turbo) on our linear response model to search for optimal parameters, aiming to minimize the difference between theoretically predicted and real LSW function values of the measured sample points.

The second algorithm, termed sBO, is a standard BO that selects sampling points with maximum information gain relative to the current state, specifically, those points that can better characterize the real LSW function given the current samples and the fitted LSW function.